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## Über dieses Buch

This book covers performance analysis of computer networks, and begins by providing the necessary background in probability theory, random variables, and stochastic processes. Queuing theory and simulation are introduced as the major tools analysts have access to. It presents performance analysis on local, metropolitan, and wide area networks, as well as on wireless networks. It concludes with a brief introduction to self-similarity. Designed for a one-semester course for senior-year undergraduates and graduate engineering students, it may also serve as a fingertip reference for engineers developing communication networks, managers involved in systems planning, and researchers and instructors of computer communication networks.

## Inhaltsverzeichnis

### Chapter 1. Performance Measures

Abstract
Modeling and performance analysis of computer networks play an important role in the design of computer communication networks. Models are tools for designers to study a system before it is actually implemented. Performance evaluation of models of computer networks gives the designer the freedom and flexibility to adjust various parameters of the network in the planning rather than the operational phase.
Matthew N. O. Sadiku, Sarhan M. Musa

### Chapter 2. Probability and Random Variables

Abstract
Most signals we deal with in practice are random (unpredictable or erratic) and not deterministic. Random signals are encountered in one form or another in every practical communication system. They occur in communication both as information-conveying signal and as unwanted noise signal.
Matthew N. O. Sadiku, Sarhan M. Musa

### Chapter 3. Stochastic Processes

Abstract
This chapter is an extension of the previous chapter. In the previous chapter, we focused essentially on random variables. In this chapter, we introduce the concept of random (or stochastic) process as a generalization of a random variable to include another dimension—time. While a random variable depends on the outcome of a random experiment, a random process depends on both the outcome of a random experiment and time. In other words, if a random variable X is time-dependent, X(t) is known as a random process. Thus, a random process may be regarded as any process that changes with time and controlled by some probabilistic law. For example, the number of customers N in a queueing system varies with time; hence N(t) is a random process
Matthew N. O. Sadiku, Sarhan M. Musa

### Chapter 4. Queueing Theory

Abstract
Queueing is simply waiting in lines such as stopping at the toll booth, waiting in line for a bank cashier, stopping at a traffic light, waiting to buy stamps at the post office, and so on.
Matthew N. O. Sadiku, Sarhan M. Musa

### Chapter 5. Simulation

Abstract
The previous chapter dealt with one of the tools for performance analysis—queueing theory. This chapter concentrates on another tool—simulation. In this chapter, we provide an overview of simulation: its historical background, importance, characteristics, and stages of development.
Matthew N. O. Sadiku, Sarhan M. Musa

### Chapter 6. Local Area Networks

Abstract
When designing a local area network (LAN), establishing performance characteristics of the network before putting it into use is of paramount importance; it gives the designer the freedom and flexibility to adjust various parameters of the network in the planning rather than the operational phase. However, it is hard to predict the performance of the LAN unless a detailed analysis of a similar network is available. Information on a similar network is generally hard to come by so that performance modeling of the LAN must be carried out.
Matthew N. O. Sadiku, Sarhan M. Musa

### Chapter 7. Metropolitan Area Networks

Abstract
With some of the characteristics of LANs and some reflecting WANs, the metropolitan area network (MAN) technology embraces the best features of both. The motivations for MAN technology include the need for: (1) interconnection of LANs, (2) high-speed services, and (3) integrated services. The proliferation of LANs and the need for connecting them has brought MANs to the fore. The increasing customer demand for high-speed services has spawned the search for new technologies with wideband transport capabilities. For example, it is important that a travel agent gets prompt responses from the host computer when making airline reservations. The salary of the agent depends on high speed data communication.
Matthew N. O. Sadiku, Sarhan M. Musa

### Chapter 8. Wide Area Networks

Abstract
A wide area network (WAN) provides long-haul communication services to various points within a large geographical area. A WAN often uses communication facilities provided by common carrier such as telephone companies. The most popular WAN is the global public switched telephone network (PSTN), which is not suitable for data transport because it was originally designed for voice. But it is rare that data networks (such the global X.25) do not interface with the PSTN. Today’s WANs are expected to integrate data, voice, and video traffic.
Matthew N. O. Sadiku, Sarhan M. Musa

### Chapter 9. Wireless Networks

Abstract
Wireless communications is one of the fastest growing fields in engineering. The last century has witnessed the introduction of many kinds of wireless networks, some of which have become the cornerstone of modern life. Such networks have provided support for nomadic and increasingly mobile users.
Matthew N. O. Sadiku, Sarhan M. Musa

### Chapter 10. Self-Similarity of Network Traffic

Abstract
In 1993, it was found out that there are modeling problems with using Markovian statistics to describe data traffic. A series of experiments on Ethernet traffic revealed that the traffic behavior was fractal-like in nature and exhibit self-similarity, i.e. the statistical behavior was similar across many different time scales (seconds, hours, etc.) [1, 3]. Also, several research studies on traffic on wireless networks revealed that the existence of self-similar or fractal properties at a range of time scale from seconds to weeks. This scale-invariant property of data or video traffic means that the traditional Markovian traffic models used in most performance studies do not capture the fratal nature of computer network traffic. This has implications in buffer and network design. For example, the buffer requirements in multiplexers and switches will be incorrectly predicted. Thus, self-similar models, which can capture burstiness (see Fig. 10.1) over several time scales, may be more appropriate.
Matthew N. O. Sadiku, Sarhan M. Musa

### Backmatter

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